风力轮机叶片的基于声信号的损坏检测-一篇评论
Shaohu Ding1, Chenchen Yang2, Sen Zhang2
1College of Mechatronic Engineering, North Minzu University, Yinchuan 750021, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究审查了用于风力轮机叶片健康监测的声信号方法. 它强调声学排放和空气动力学噪声,用于早期检测和定位损害,增强风能可持续性.
科学领域:
- 工程 工程师 工程师 工程师
- 材料科学 材料科学 材料科学
- 声学 声学 在声学方面
背景情况:
- 风力轮机叶片健康监测对全球风能行业至关重要.
- 早期检测叶片损坏可以防止进一步恶化,并延长使用寿命.
- 现有的检测方法在实时和远程监控方面面临挑战.
研究的目的:
- 审查和分析基于声信号的风力轮机叶片结构完整性检测方法.
- 调查声辐射 (AE) 和空气动力学噪声信号对于损坏检测和定位的潜力.
- 探索用于自动分类风力轮机叶片故障机制的机器学习算法.
主要方法:
- 审查现有的风力轮机叶片检测技术.
- 对声辐射 (AE) 信号检测用于裂和故障检测的分析.
- 评估用于实时,远程监控的空气动力学噪声信号检测.
- 集成机器学习算法用于自动化故障分析.
主要成果:
- 声波发射 (AE) 信号可提供风力轮机叶片中裂和生长故障的早期检测.
- 空气动力学噪声信号提供了方便的实时和远程监控功能.
- 机器学习增强了刀片故障机制的自动检测和分类.
结论:
- 基于声信号的方法,包括AE和空气动力学噪声,对非破坏性,远程和实时风力轮机叶片健康监测具有前景.
- 这些技术在早期损坏检测,定位和运营可持续性方面具有优势.
- 未来的趋势指向先进的机器学习集成,用于全面的叶片健康管理.
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